Energy and data-efficient online time series prediction for predicting evolving dynamical systems are critical in several fields, especially edge AI applications that need to update continuously based on streaming data. However, current DNN-based supervised online learning models require a large amount of training data and cannot quickly adapt when the underlying system changes. Moreover, these models require continuous retraining with incoming data making them highly inefficient. To solve these issues, we present a novel Continuous Learning-based Unsupervised Recurrent Spiking Neural Network Model (CLURSNN), trained with spike timing dependent plasticity (STDP). CLURSNN makes online predictions by reconstructing the underlying dynamical system using Random Delay Embedding by measuring the membrane potential of neurons in the recurrent layer of the RSNN with the highest betweenness centrality. We also use topological data analysis to propose a novel methodology using the Wasserstein Distance between the persistence homologies of the predicted and observed time series as a loss function. We show that the proposed online time series prediction methodology outperforms state-of-the-art DNN models when predicting an evolving Lorenz63 dynamical system.

翻译：能量与数据高效的在线时间序列预测方法对于预测演化中的动态系统至关重要，尤其在需要基于流数据持续更新的边缘人工智能应用中。然而，当前基于深度神经网络的监督式在线学习模型需要大量训练数据，且无法在底层系统变化时快速适应。此外，这类模型需利用新流入的数据进行持续重训练，导致其效率低下。为解决上述问题，我们提出了一种新型的基于持续学习的无监督递归脉冲神经网络模型（CLURSNN），该模型采用脉冲时序依赖可塑性（STDP）进行训练。CLURSNN通过测量递归脉冲神经网络层中间介数中心性最高的神经元膜电位，利用随机延迟嵌入重构底层动态系统，从而实现在线预测。我们还引入拓扑数据分析，提出一种创新方法：将预测序列与观测序列的持续同调之间的沃瑟斯坦距离作为损失函数。实验表明，在预测演化中的洛伦兹63动态系统时，所提出的在线时间序列预测方法性能优于当前最先进的深度神经网络模型。